Lead AI Full-Stack Engineer

We are looking for a Lead AI Full-Stack Engineer to define the technical vision, architecture, and execution strategy for our next-generation AI-native platform. In this high-impact role, you will lead a cross-functional team of developers while remaining hands-on in code. You will own end-to-end technical direction across our Java and Python Microservices, enterprise React applications, cloud systems (Azure), and advanced LLM/RAG orchestration pipelines., * Technical Leadership & Strategy: Establish end-to-end AI application architecture, establish best practices for prompt engineering, latency optimization, cost control, and set engineering standards across frontend, backend, and AI stacks.

• Team Mentorship & Delivery: Guide and mentor cross-functional engineers (5-10 team members), conduct code reviews, unblock technical issues, and partner with Product and Design leaders to map product roadmaps into technical deliverables.
• AI & RAG System Architecture: Architect resilient Retrieval-Augmented Generation (RAG) pipelines, multi-agent frameworks, and real-time semantic search using tools like LangChain, LlamaIndex, or AutoGen.
• Full-Stack & Microservices Design: Oversee robust, scalable Microservices engineered in Java (Spring Boot) and Python (FastAPI/Django), integrated with component-driven React/TypeScript frontends.
• Enterprise Azure Infrastructure: Own cloud architecture strategy on Microsoft Azure (Azure OpenAI, Azure AI Search, AKS, Container Apps), prioritizing high availability, strict security protocols, and cost governance.
• AI Governance & Reliability: Implement guardrails for LLM safety, PII detection, fallback mechanisms, hallucination evaluation metrics, and continuous performance monitoring.

• Experience: 7+ years in full-stack engineering, including 2+ years leading engineering initiatives or technical teams and building AI-native applications in production.
• Frontend: React.js, TypeScript, state management architectures, micro-frontends, and web performance optimization.
• Backend: Mastery of Java (Spring Boot) and Python (FastAPI, Flask); expertise in Microservices design, asynchronous patterns, and API gateways.
• AI / LLM Orchestration: Deep expertise with RAG architectures, Vector DBs (Pinecone, Qdrant, Azure AI Search, pgvector), agentic workflows, model routing, and token optimization.
• Cloud & DevOps: Advanced skills in Azure cloud infrastructure, Docker, Kubernetes (AKS), Infrastructure-as-Code (Terraform/Bicep), and CI/CD automation.
• Leadership: Track record of mentoring developers, driving architectural decisions, and communicating complex AI tradeoffs to executive leadership.
• Proven experience fine-tuning open-source models (Llama, Mistral) or building enterprise-wide semantic caches.
• Experience with multi-agent design patterns (AutoGen, CrewAI) and complex function-calling structures.
• Deep knowledge of enterprise AI compliance, data privacy, and Responsible AI frameworks.